Enhanced experimental investigation of threshold determination for efficient channel detection in 2.4 GHz WLAN cognitive radio networks

This paper presents an experimental investigation of threshold determination for efficient channel detection in wireless LAN (WLAN) based cognitive radio (CR) networks. The spectrum saturation problem is a critical issue in wireless communication systems worldwide due to on growing user demands day...

Full description

Saved in:
Bibliographic Details
Main Authors: Morshed, Mohammad Nayeem, Sabira, Khatun, Latifah Munirah, Kamarudin, Syed Alwee, Aljunid, R. Badlishah, Ahmad, Ammar, Zakaria, Fakir, Md Moslemuddin
Format: Article
Language:English
English
Published: IAMOT 2017
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/29109/1/Enhanced%20experimental%20investigation%20of%20threshold%20determination%20for%20efficient%20.pdf
http://umpir.ump.edu.my/id/eprint/29109/2/Enhanced%20experimental%20investigation%20of%20threshold%20determination%20for%20efficient_FULL.pdf
http://umpir.ump.edu.my/id/eprint/29109/
https://www.ijmot.com/VOL-12-NO-5.aspx
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents an experimental investigation of threshold determination for efficient channel detection in wireless LAN (WLAN) based cognitive radio (CR) networks. The spectrum saturation problem is a critical issue in wireless communication systems worldwide due to on growing user demands day by day with many new applications to the limited frequency spectrum. Hence, present demand is an efficient and intelligent spectrum management and allocation system. In this paper, we proposed an adaptive threshold determination technique based on free space path loss (FSPL) model to detect the presence or absence of PUs. The model is designed especially for Android based smartphones and tablets. The smartphones act as secondary users (SUs) and existing 2.4 GHz WLAN channels as PUs. The network is prepared in a usual noisy lab/outdoor environment and tested for the robustness of the proposed model. Results show the desired range of usable threshold and the channel detection performance depends on the noise floor level of the surrounding environment.